Price charting system and technique

ABSTRACT

The invention is directed to a market price charting method for displaying both current and historical price activity in terms of valuation rather than absolute price. The invention allows users to quantify the degree in which a market is overbought or oversold. Results may be displayed electronically or on a hard copy for viewing or used in additional analysis of a market.

FIELD OF THE INVENTION

The present invention relates generally to financial data processing andanalysis, and more specifically, to an internet or acomputer-implemented system and technique for producing enhanced pricecharts to allow investors to quickly and easily analyze the relativeoverbought or oversold state of any market and for generatingquantifiable relative overbought and oversold price levels to drive(feed) mathematical trading systems or help investors strategicallyenter or exit markets.

BACKGROUND OF THE INVENTION

In today's fast-paced financial markets, investors need to accessinformation quickly and easily in order to process trading decisions.With the significant growth of online trading, individual investors needeffective market analysis tools to help them make better tradingdecisions. Because the saying “a picture is worth a thousand words”still holds true, traders all over the world rely on traditional barcharts to display both past and present price activity. Bar charts arevaluable because they reflect the history of price movement in an easyto process format (a picture.) An investor can literally analyze a chartin a glance. Although bar charts have proven to be valuable tools in theinvestment field, a frequently asked question is “are traditional bar(price) charts alone the most effective way define relative overboughtprice levels, relative oversold price levels, or fair value?” As will beshown, price can be displayed in a format which makes is possible todefine the relative valuation of any market.

With the advancement in personal computers, the Internet, and onlinetrading, trading in the stock (bonds, and futures) market hassignificantly increased in popularity. Investors have significantresources to utilize when determining what stock to buy or sell.However, until now, investors have not had a powerful charting tool thatcan quantify relative value and identify optimal market entry or exitprice levels. A market analysis tool that can identify relativeoverbought and oversold price levels will potentially allow investors tolower their risk exposure (to loss) by helping buyers to enter marketsat relatively oversold (undervalued) price levels and sellers to exitmarkets at relatively overbought (overvalued) price levels. Thus, buyingat lower price levels and selling at higher price levels a trader isable to enhance his or her profit potential.

Furthermore, with the recent advancements in computers, many traders arenow developing mathematical computerized trading systems. These tradingsystems rely on quantifiable price levels to generate buy and sellsignals. Until now, the most common quantifiable price levels used todrive trading systems have been the opening or closing price of a timeperiod (day, week, month, 10-minute bar, etc.). The previous day's (ortime period's) highs and lows have also been used as quantifiablereference price levels to direct trading systems to enter or exitmarkets. Any method or market analysis technique that could expand thenumber of quantifiable price levels to drive mathematical tradingsystems would be extremely useful to trading system designers.

SUMMARY OF THE INVENTION

Meeting these requirements, the present invention, which includesproducing Value Charts™ (sometime hereafter designated “VC”) and PriceAction Profile™ (sometime hereafter designated “PAP”), has the potentialto revolutionize price charting, online (internet) trading, and tradingsystem design.

INTRODUCTION

The primary purpose of this summary is to present the Value Chart™ andPrice Action Profile™ technical indicators in an easy to understandformat.

Value Charts™ and Price Action Profile™ were designed with both thenovice investor and the seasoned investor in mind. Because it was oncesaid that “a picture is worth a thousand words,” most traders relyheavily on traditional price charts and graphical indicators. Charts andgraphs condense information and allow traders to quickly digest bothpast and present market activity. As will be described, Value Charts™and Price Action Profile™ reveal a hidden order in the markets. Thesemarket analysis tools do not represent the “black box.” Rather, theyrepresent a valuable addition to any trader's arsenal of technicalindicators. These powerful new charting techniques will allow traders ata glance to gain insight into the relative valuation of any free market.

For trading system developers, Value Charts™ opens up a whole newhorizon of reference price levels that can be utilized to drive tradingsystems or indicators. Until now, most traders have had access to only alimited number of reference prices levels. Reference price levels areprimarily used to either instruct a trading system when to enter or exita position or they are used to calculate an indicator. For daily barcharts, the reference price levels include the opening price of the day,the closing price of the day, or a previous day's high price or lowprice. By utilizing Value Charts™, traders can now create tradingsystems that enter or exit markets at relative quantifiable pricelevels. The present invention provides the ability to calculate relativequantifiable price levels during a trading day or other time period tofacilitate trading system development.

Quantifiable information is useful information. Many market analysisstrategies rely too heavily on the opinion of the user when determiningwhen certain rules or conditions are met. Market analysis strategiesthat rely on the judgement of a trader when determining trading signalsoften contain too much “gray area” and have little long-term usefulness.Value Charts™ and Price Action Profile™, on the other hand, generatequantifiable information that can only be interpreted one way. Theseinnovative new market analysis tools were developed for anyone,regardless of trading experience, to learn and use.

The Limitations of Traditional Price Charts

Most traders and market analysts utilize some form of traditional pricecharts to analyze markets. This is primarily because price charts areeasy to read and they condense information, which saves us time. Themost common form of traditional price charts is the bar chart, whichdisplays the open, high, low, and close of the market underconsideration (as seen in FIG. 1).

The S&P 500 futures market in FIG. 1 serves as a good example of atraditional bar chart. As we all know, each price bar is plotted withrespect to zero. Zero serves as the reference point for everytraditional price chart. For example, the S&P 500 market closed at1440.70 above zero. These charts are valuable for displaying both thecurrent and the historical price activity of a market. Traditional pricecharts, however, are not effective in identifying relative value. Inhindsight we can identify overbought and oversold price levels but theyare all but impossible to identify real-time using traditional barcharts. Because the goal of every market participant is to enter into amarket at a good value, we must develop a way to present price bars thateffectively define a good value.

Four daily price bars are shown in FIG. 2, with the last three dailyprice bars B, C, and D appearing similar. In fact, the last three pricebars represent different values even though they have the same open,high, low, and closing prices.

At first this statement may not make a lot of sense. Analyzing the lastthree price bars, it is noted that they are different because each pricebar represents a different day (time period). When the last three daysare analyzed in terms of value, day (C) is a better value than day (B)and day (D) is a better value than days (B) and (C). If you were lookingto buy into the market displayed in FIG. 2 after the first day (pricebar A) has ended, which price bar close would you select to enter a longposition on, if only given the choice between days B, C, or D? Becausedays B, C, and D all traded at identical prices, day D would representthe best value. This can be explained by the fact that your cost ormargin, the money necessary to buy into the market, could have been leftin an interest bearing account or another investment for days B and Cinstead of in the market where no profits would have been generated.Although traditional bar charts present the last three bars in a waythat they appear to be at the same relative value, each day actually hasa different relative value. Value is established in large part by ourmemory of recent price history. If we have seen a market trade at thesame price for several days, we might feel more comfortable in payingthat price ourselves because the price has been established as anacceptable, or fair, value by the market. So we as humans establishvalue in large part by what others have regarded as value, which isreflected in historical price activity. This observation will becomeapparent as the Value Charts according to the present invention aredescribed.

Fair value in the market place is confirmed by time. If a market tradedat the same price forever, one would logically assume that the buyersand sellers agree that the price is not overbought or oversold, but atfair value. The markets that we participate in are rarely ever tradingat the exact same price over time, but instead they are constantlyovershooting fair value, both to the up side and the down side, acrossevery time frame. Furthermore, because we live in a constantly changingworld, fair value is constantly being redefined as time goes on.Actively traded markets are always oscillating around fair value andfair value is constantly being redefined over time. Furthermore, it ispossible for a market to be undervalued with respect to a short-termtime frame and overvalued with respect to the long-term time frame. Thisphenomenon can be partly explained by the fact that short-term tradersreference recent price activity when they seek to define value andlong-term traders reference long-term price activity when seeking todefine value. Furthermore, short-term fundamentals and long-termfundamentals can be different influencing factors on a market.

For example, with no change in the fundamental picture, a short-termtrader may believe that corn is relatively undervalued and buy severalcorn contracts for his account at $3.00 a bushel. Being short-term innature, he is quick to liquidate as soon as corn prices rise severalcents to $3.15 a bushel. His decision to sell his profitable position isbased on the fact that the relative value of corn prices has changedwith respect to his perception. When corn was at $3.00 a bushel, earlierin time, he viewed the corn market as being undervalued with respect tothe short-term timeframe. This may be because he remembers recent cornprices trading above $3.20 a bushel. A long-term trader who is lookingto buy corn may not see $3.00 a bushel as a good value because hereferences long-term historical prices of corn and remembers when cornwas trading around $2.00 a bushel one year earlier. Our historicalreference prices, whether short-term or long-term, are built into ourmemories and we subconsciously reference them when we seek to establishvalue.

The market, made up of many individual participants, is in a constantsearch for fair value across every time frame. We will primarily concernourselves with the analysis of short-term fair value when describing theValue Charts™ and Price Action Profile™ in this description. It wouldseem logical that most of the trading would occur around the fair valueprice level. Prices, as previously stated, are constantly oscillatingfrom degrees of relative overvalue to degrees of relative undervalue andback to overvalue. Although traditional bar charts are useful whentrading, they are not useful in identifying relative overbought andrelative oversold price levels. As previously noted, two identical pricebars on a traditional bar chart do not necessarily equate to two equalvalues. Knowing this, a graphical charting tool that can effectivelyidentify relatively overbought price levels, relatively oversold pricelevels, and fair value would be desirable. This need to accomplish whattraditional bar charts are unable to accomplish is provided by thepresent invention.

Like traditional bar charts, Value Charts™ allow investors to quicklyanalyze the historical characteristics of any market. However, inaddition to this, Value Charts™ along with Price Action Profile™ allowinvestors to quickly analyze the current relative overbought or oversoldstate of any market with little effort. These innovative new marketanalysis techniques give investors the ability to quickly determinewhether a market is overbought, oversold, or trading at fair value. In apowerful new way, these market analysis tools allow investors toquantify value in a market. In addition to these benefits, Value Charts™now make it possible for trading system developers to createmathematical trading models that can generate buy or sell signals atValue Chart™ price levels, not just the at the open or close of atrading session. Furthermore, by using Value Charts™ and Price ActionProfile™, computers can now scan thousands of markets to search formarkets that are currently overbought or oversold, as defined by ValueCharts™. Traders can use this new technology to track the overbought oroversold state of any market across different time frame price bars(i.e. 30-minute, daily, weekly, monthly, etc.).

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a daily futures bar chart typical of the prior art for aparticular market.

FIG. 2 shows four daily price bars.

FIG. 3 shows a daily price chart (top) and relative price chart (bottom)for a particular market.

FIG. 4 Example calculations for generating the relative chart of FIG. 3.

FIG. 5 shows a daily price chart (top) and relative price chart (bottom)for another market.

FIG. 6 shows a daily price chart (top) and a Value Chart™ (bottom)according to a preferred embodiment.

FIG. 7 shows example calculations for generating the Value Chart™ shownin FIG. 6.

FIG. 8 shows a daily bar chart above a daily Value Chart™.

FIG. 9 shows a bar chart over a Value Chart™ (middle) and a relativechart (bottom).

FIG. 10 shows a bar chart in 1982 over a Value Chart™ and a relativechart for the S & P 500.

FIG. 11 shows a bar chart in 1999 over a Value Chart™ and a relativechart for the S & P 500.

FIGS. 12( a)-12(d) show development of a Price Action Profile™ accordingto a preferred embodiment.

FIG. 13 a shows a daily bar chart above a daily Relative Chart.

FIG. 13 b shows a frequency histogram of the relative daily chart shownin FIG. 13 a.

FIG. 14 a shows a daily bar chart above a daily Value Chart™.

FIG. 14 b shows a Price Action Profile™ generated from the daily ValueChart™ of FIG. 14 a.

FIG. 15 shows an alternate Price Action Profile™ from a daily ValueChart™.

FIG. 16 shows The Empirical Rule describing normal (mound-shaped) BellCurves.

FIG. 17 shows a daily Value Chart™ with a +12 Value Chart™ line.

FIG. 18 shows a daily Value Chart™ with ±2 Value Chart™ lines.

FIG. 19 shows a daily Value Chart™ with a −8 Value Chart™ line.

FIG. 20 shows a Price Action Profile™ from a daily Value Chart™.

FIG. 21 shows a Price Action Profile™ analysis generated from severaldifferent markets.

FIG. 22 shows a Price Action Profile™ relative value convention.

FIG. 23 a shows a Daily Treasury Note price chart (above) and ValueChart™ (below).

FIG. 23 b shows a Valuation convention—Price Action Profile™ & ValueChart™.

FIG. 24 shows a Price Action Profile™ analysis comparison—S&P 500 (1980svs. 1990s).

FIG. 25 shows a Monthly price chart of Cocoa (box encloses the 1970s).

FIG. 26 shows Buy points (identified by Value Charts™) with low riskexposure.

FIG. 27 shows Average worst exposure profitability graph from buy pointsin FIG. 26 (x-axis represents days following average market entry).

FIG. 28 shows Low risk exposure buying point on daily American Expressprice chart.

FIG. 29 a shows Trend-following system whipsawed in Soybeans.

FIG. 29 b shows how Value Charts™ can improve fill prices in choppymarkets.

FIG. 30 shows Profits from Value Chart™ enhancements for whipsaw trades(FIGS. 29 a,b).

FIG. 31 is a diagram that illustrates six different ways a price bar cantrade relative to a Value Chart™ interval.

FIG. 32 is an example trading screen of the Value Charts™ and PriceAction Profile™ concept applied to a daily chart of a market.

FIG. 33 is an example trading screen of the Value Charts™ conceptapplied to a weekly chart of a market (data window is displayed).

FIG. 34 is an example trading screen of the Price Action Profile™concept applied to a weekly chart of a market (displayed in FIG. 33).

FIG. 35 is an example trading screen of the Value Chart™ and PriceAction Profile™ concept applied to a monthly chart of a market.

FIG. 36 is an example trading screen of the closing price and the ValueChart™ close and Price Action Profile™ (line) concept applied to a dailychart of a market.

FIG. 37 shows an example trading screen of the closing price and theValue Chart™ close concept applied to a daily chart of a market.

FIG. 38 a shows daily Coca Cola with Value Chart™.

FIG. 38 b shows monthly Coca Cola with Value Chart™.

FIG. 39 shows Price Action Profiles™ of Daily and Monthly Coca ColaValue Charts™ through Oct. 8, 1999.

FIG. 40 displays the current valuation of the Crude Oil market acrossseveral different timeframes as defined by Price Action Profile™.

FIG. 41 displays the current valuation of the Crude Oil market acrossseveral different timeframes as defined by Price Action Profile™.

FIG. 42 displays the current valuations of several user defined markets(in this case grains) as defined by Price Action Profile™.

FIG. 43 displays the current valuations of several user defined markets(in this case grains) as defined by Price Action Profile™.

FIG. 44 displays a Value Chart™ transposed into an absolute chart shownon the bottom portion thereof.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

Value Charts™

Although traditional bar charts are useful for referencing thehistorical and current price activity of a market, we need to have theability to chart price in a new way so that they can clearly identifyrelative overbought and oversold price levels. In other words, we needto chart price bars on a relative basis instead of an absolute basis.

In this type of chart, such a relative basis may be based on a simple5-day moving average of the median bar chart price (see Formulas forcalculating a Relative Chart below) as the reference axis. Therefore,instead of plotting price with respect to zero, the price is plotted(open, high, low, and close) with respect to this simple 5-day medianmoving average, which we will refer to as the floating axis. Price givenin terms of its relation to the floating axis, instead of zero, will bereferred to as relative price (see FIG. 3).

Formulas for Calculating a Relative Chart

Median Price=(High+Low)÷2

Floating Axis=5-day moving average of Median Price

Relative Price=Price−Floating Axis

The date, open, high, low, and close from the daily AT&T bar chart (FIG.3) are listed in the first five columns of FIG. 4. By utilizing theFloating Axis formula displayed above, we are able to calculate theFloating Axis in column 6. From this point calculating relative price issimple (reference FIG. 4). For example, we simply take the open price incolumn 2 and subtract the Floating Axis value in column 6 to get therelative open in column 7. Simply repeat this process for the high, low,and close to calculate the relative high, relative low, and relativeclose.

From FIG. 4 we can calculate the relative open for Aug. 31, 1999 (firstRow) as follows:

$\begin{matrix}{Open} & \; & {{Floating}\mspace{14mu}{Axis}} & \; & {{Relative}\mspace{14mu}{Open}} & \; \\46.437 & - & 47.894 & = & {- 1.457} & \;\end{matrix}$

Now with the basic understanding of how to calculate a relative barchart, we can view a relative chart displayed below a Soybeanstraditional bar chart. This example, which displays a major bull market,will illustrate how volatility can significantly increase as a bullmarket progresses. FIG. 5 displays a daily Soybeans bar chart that ispositioned directly above a daily Soybeans relative bar chart. Once thenormal bar chart prices were converted into relative prices, they wereplotted directly below their corresponding normal prices (FIG. 5) sothat the traditional price chart is plotted directly above the relativeprice chart. This new relative chart reflects the relative movement ofthe Soybeans market with respect to the floating axis. When thetraditional price bars move farther away from the floating axis in thetop half of the chart, the relative price bars move farther away fromthe zero line in the bottom half of the chart.

Note that the moving average line in the top chart, which represents thefloating axis, is equivalent to the zero line in the relative chartdisplayed below. Imagine pulling the curvy moving average line (floatingaxis) in the top half of FIG. 5 until it was straight. This wouldtransform the traditional chart into the relative chart. Upon a closeinspection of the Soybeans relative chart, it is evident that pricetends to deviate more from the zero line (floating axis line) as marketsbecome more volatile. Note that in FIG. 5, as the price of Soybeansbecame much more volatile on the right side of the chart (June throughAugust timeframe) the relative price bars in the lower chart deviatedmuch farther from the zero axis. Remember, the zero axis in the RelativeChart (lower chart) represents the floating axis, which is the movingaverage in the top chart. The relative price chart clearly did notadjust to changing volatility conditions and therefore was of littlevalue because we want the relative chart to be able to define how faraway from the zero line we should typically expect the relative pricebars to deviate. Because the expected deviation from the zero line isalways changing with market volatility, relative charts will not beeffective in defining overbought and oversold relative price levels.

In order for a relative chart to be more useful in identifyingoverbought and oversold price levels, it is desirable in the preferredembodiment to have the ability to adapt to changing market volatility.This is accomplished in providing the y-axis units in the Relative Chart(displayed in FIG. 5) defined in terms of a dynamic volatility unitinstead of a static price unit. This new dynamic volatility unit allowsvolatility adjusted relative charts to be useful in defining relativeoverbought and oversold price levels as market volatility changes overtime. These now volatility adjusted relative charts are hereafterreferred to as Value Charts™.

In the preferred embodiment, the dynamic volatility unit (DVU) isdefined as 20% of the 5-day average of the volatility measurement (VM).The VM is defined as either the daily price range (High-Low) or today'sclose minus yesterday's close (C-C[1]), whichever is larger. We can nowapply a Value Chart to the AT&T price chart displayed in FIG. 3 for thepurpose of understanding the calculations behind Value Charts™. Theformulas used to create a Value Chart™ from traditional bar chart pricesare listed below under formulas for calculating a Value Chart™.

Formulas for Calculating a Value Chart

Floating Axis=5-day moving average of ((High+Low)÷2)

Dynamic Volatility Units (DVU)

If (High-Low)>(Close-Close[1]) then VM=(High-Low)

If (High-Low)<(Close-Close[1]) then VM=(Close-Close[1])

{Close-Close[1] means today's close minus yesterday's close}

Dynamic Volatility Units (DVU)=(5-day moving average of VM)*0.20

Value Price=(Price−Floating Axis)÷DVU

Like before, the date, open, high, low, and close of the daily AT&T barchart (as seen in FIG. 6) are listed in the first five columns of FIG.7. By utilizing the Floating Axis formula, the Floating Axis iscalculated in column 6 (refer to FIG. 7 for example calculations). Byutilizing the Dynamic Volatility Unit formula, the Dynamic VolatilityUnit (Volatility Unit) in column 7 (of FIG. 7) is calculated. From thispoint calculating Value Chart price is simple. For example, we simplytake the open value in column 2 and subtract the Floating Axis value incolumn 6, and then divide this value by the Dynamic Volatility Unit incolumn 7 to get the Value Chart Open (Value Open) price in column 8.Simply repeat this process for the high, low, and close to calculate theValue Chart High, Value Chart Low, and Value Chart Close.

From FIG. 7 we can calculate the Value Chart Open for Aug. 31, 1999 asfollows:

$\begin{matrix}{Open} & \; & {{Floating}\mspace{14mu}{Axis}} & \; & {{Volatility}\mspace{14mu}{Unit}} & \; & {{Value}\mspace{14mu}{Chart}\mspace{14mu}{Open}} \\\left( 46.437 \right. & - & \left. 47.894 \right) & \div & 0.455 & = & {- 3.202}\end{matrix}$

The formulas above allow us to now convert the traditional Soybeansdaily price chart in FIG. 5 into a volatility adjusted relative pricechart, or Value Chart™ (FIG. 8). As you can see, the daily SoybeansValue Chart™ in FIG. 8 is significantly different from the relativeprice chart displayed in FIG. 5. You can see in FIG. 8 that the ValueChart™ is effective in adjusting to changing volatility levels in theSoybeans bull market. It is not uncommon for markets to dramaticallyincrease in volatility when they reach higher price levels. In fact, weall know that the volatility characteristics of every free market tendto change over time along with changing price. A Value Chart™ has theability to adapt to different levels of volatility in any free marketand still effectively define overbought and oversold relative pricelevels. In other words, as volatility changes, the point deviation fromthe zero line in a Value Chart™ that would constitute an overbought oroversold price level would theoretically remain the same. ThereforeValue Charts™ can effectively identify overbought and oversold pricelevels in the Soybeans bull market in February 1988 (when marketvolatility is lower) just as well at it can effectively identifyoverbought and oversold price levels in August 1988 (when marketvolatility is higher).

Now we can begin to further understand how to use Value Charts™ whenanalyzing markets. A Value Chart™ is usually positioned directly belowthe traditional price chart that it is generated from. The Value Chart™can also be displayed without the traditional bar chart. FIG. 9 displaysboth the Value Chart™ and the relative chart directly below a daily barchart of the 1988 Soybeans bull market. During the February-March timeperiod the relative chart deviated from zero +17 cents to the upside and−17 cents to the downside. During the July time period, when Soybeansexperienced peak volatility, the relative chart deviated from zero +66cents to the upside and −77 cents to the downside. These relative chartvalues represent a 390% increase in upside deviations and a 450%increase in downside deviations from the mean (zero) when the Soybeansmarket dramatically increased in volatility from the earlier stages ofthe bull market to the climax of the bull market. On the other hand,during the February-March time period the Value Chart™ deviated fromzero +9.7 dynamic volatility units to the upside and −10.2 dynamicvolatility units to the downside. During the July time period, whenSoybeans experienced peak volatility, the Value Chart™ deviated fromzero +10.7 dynamic volatility units to the upside and −9.5 dynamicvolatility units to the downside. The Value Chart™ was successful inadjusting to dramatically increased volatility in the Soybeans marketover the course of the 1988 bull market.

The ability of Value Charts to adjust to changing market volatilityallows us to define overbought and oversold price levels even whenmarket volatility changes dramatically over time. As shown, Value Chartscan adjust to increasing volatility within a single bull market, butalso can adapt to the steadily increasing volatility seen in the S&P 500futures market over two decades. A major bull market lastingapproximately 20 years in the S&P 500 futures market results in anincrease of more than 400% in the value of this contract. This resultsin a dramatic increase in market volatility and price swings, whichrepresent the perfect testing ground for the Value Charts™ and PriceAction Profile™ concepts.

Notice in FIG. 10 that the S&P 500 relative chart during 1982experienced deviations from zero to as high as +9.0 and as low as −6.1.However, seventeen years later in 1999 when the S&P 500 market was atmuch higher price and much higher volatility levels (FIG. 11), the S&P500 relative chart experienced deviations from zero as high as +59.9 andas low as −54.0. In this example, the market volatility increasedsignificantly over the course of almost two decades. Now, referring toFIG. 10, we note that the Value Chart™ experienced deviations from zeroas high as +12.5 and as low as −10.3 whereas in 1999 (FIG. 11), theValue Chart™ experienced deviations from zero as high as +11.0 and aslow as −11.0. Value Charts™ were successful in adapting to the slowlyincreasing levels of volatility in the S&P 500 futures markets.

Incredibly, the Value Chart™ in FIG. 9 has the same scale that the ValueCharts™ have in both FIG. 10 and FIG. 11. Value Charts™ are able toadapt to changing market volatility and, at the same time, work on anyfree market in the same manner. As will be described, this fact is verysignificant because as Value Charts™ can identify relative overboughtand relative oversold price levels in any free market. This willpotentially allow traders to enter and exit markets at better, or moreprofitable, price levels. Furthermore, traders can now design tradingsystems that enter or exit markets at Value Chart™ price levels. BecauseValue Charts™ works the same across every market by using the sameuniversal overbought and oversold point scale, trading strategies nolonger have to be revised to accommodate each unique market.

It is known that, when market volatility increases, price fluctuationsalso increase. Given this fact, it would be expected that withincreasing volatility levels, short-term overbought and oversold pricelevels would be characterized by even greater price deviations from amoving average reference point. In other words, a trader would expectmarket corrections and fluctuations to increase as volatility increased.With the development Value Charts™ and Price Action Profile™, tradersare provided with a powerful market analysis tool that is capable ofdefining value. In the following section, it is described how PriceAction Profile™ is used to verify the effectiveness of the dynamicvolatility units in Value Charts™ and also how Price Action Profile™ canbe used as a effective compliment to the Value Charts™ concept indeveloping powerful trading strategies.

Price Action Profile™

In the previous section it was observed that a Value Chart™ can be apowerful market analysis tool in identifying relative overbought andoversold price levels. The ability of Value Charts™ to adapt to changingvolatility conditions in the markets and function effectively across anarray of significantly different markets makes the present invention apowerful market analysis tool. However, a Value Chart™ can be powerfullyenhanced with Price Action Profile™. Price Action Profile™ is simply aprofile, or bell curve, that describes the historical behavior of aValue Chart™. Price Action Profiles™ display how frequently a ValueChart™ has traded above, below, or in any given Value Chart™ sector.

Creating a Price Action Profile™

Recall that a Price Action Profile™ reflects the distribution of pricebars in the different volatility intervals of a Value Chart™. The ValueChart™ bars are simply “piled up” on one side, preferably left, of theValue Chart™. FIGS. 12 a-12 d demonstrate the development of a PriceAction Profile™ from a Value Chart™. Ignore the thickness of the barsthat build the Price Action Profile™ (left) and focus on how they pileup in FIGS. 12 a, 12 b, 12 c, and 12 d.

In FIG. 12 a you will note that the Price Action Profile™ of just oneValue Chart™ bar is simply a bar-shaped profile. The addition of manymore bars is what generates a bell curve shape in a Price ActionProfile™. Observe the addition of a second, third, and forth ValueChart™ bar in FIG. 12 b, FIG. 12 c, and FIG. 12 d. Note how theadditional bars effect the shape of the Price Action Profile™.

Understanding how the Price Action Profile™ is generated is fairlysimple. When you examine FIG. 12 d you can count how many bars trade ineach volatility interval. For example, the first three bars in the ValueChart™ trade in the (+1) volatility interval. Notice that the PriceAction Profile™ reflects this by having three layers in the (+1)volatility interval. As price bars are added to the Value Chart™, thePrice Action Profile™ will continue to stack these bars and eventuallyform the shape of a mound-shaped bell curve.

Validating Value Charts™

Recall from FIG. 5 that the relative chart was the predecessor to theValue Chart™. Price Action Profiles™ facilitated determining thatrelative charts can be improved by conversion into Value Charts™, whichare very valuable from a technical analysis standpoint. Price ActionProfiles™ of both the relative chart as shown in FIG. 5 and the ValueChart™ as previously displayed in FIG. 8 are shown on the followingpages.

In order for a relative chart to be useful in identifying overbought andoversold price levels, it would have to have the ability to adapt tochanging market volatility. The profile, or distribution (FIG. 13 b),generated from the relative price activity in the Soybeans market (FIG.13 a) is of little statistical value because of its thorn like shape.This is understandable given that relative prices are always in aconstant state of deviating more or less from the zero axis as marketvolatility changes. Therefore, historical relative price activity is oflittle value because historical volatility was most likely differentfrom the present market volatility. It would be preferred to compareapples to apples, and relative charts simply do not allow us to do this.

In the preferred embodiment therefore, a Price Action Profile™ isgenerated for the Value Chart™ displayed in FIG. 14 a. The chart in FIG.14 a only displays about nine months of price datum. In the preferredembodiment, building a profile from much more extensive Value Chart™price activity would be advantageous. Again, building a profile simplyinvolves stacking or sliding all of the Value Chart™ daily price bars tothe left of the screen. In this example, we will evaluate approximately30 years of price datum as we develop this frequency diagram or bellcurve of Value Chart™ price activity (displayed in FIG. 14 b). Theprevious profile generated from non-volatility adjusted y-axis intervals(relative chart) was of little use from a statistical standpoint.However, as you can see, this new profile, which will be referred to asPrice Action Profile™, was derived from the Value Chart™ price datum andis statistically valid in that it closely resembles the shape of amound-shaped, or normal bell curve. This is very significant in thatnormal bell curves derived from a sample of datum are very useful indetermining future behavior of a given population (subject matter). Inother words, we can expect a future Price Action Profile™ to closelyresemble a past Price Action Profile™ for a given market, as the ValueCharts™ are effective in adapting to changing market volatility.

As you can see in FIG. 14 b, the Price Action Profile™ resembles a bellcurve whereas the profile generated by the non-volatility adjustedrelative price chart in FIG. 13 b resembled a thorn. Because the PriceAction Profile™ in FIG. 14 b closely resembles a normal bell curve, wecan now make inferences about the population (future Soybean prices) byanalyzing this bell curve. As should be evident, having insight on thefuture price behavior of any market can lead to trading profits. Byanalyzing the Price Action Profile™, the trader can quantify thefrequency in which the Soybean market trades in each Value Chart™interval (analysis seen in FIG. 15).

It is noted that normal (or mound-shaped) bell curves tend to haveapproximately 68% of their distribution between +1 standard deviation,approximately 95% of their distribution between ±2 standard deviations,and approximately all of the distribution within ±3 standard deviations(see FIG. 16). Because this is a rule of thumb, a rule that is observedto work in practice, it has been called the Empirical Rule (FIG. 16).The analysis displayed in FIG. 15 shows that the Soybeans Price ActionProfile™ has approximately 70% of its distribution between ±1 standarddeviation (S=4.26), approximately 96% of its distribution between ±2standard deviations (2S=8.52), and approximately 100% of itsdistribution within ±3 standard deviations (3S=12.78). These resultsqualify the Soybeans Price Action Profile™ as a statistically normalbell curve that would be useful in making inferences about futureSoybeans Value Chart™ price activity.

The exact shape of the bell curve is not important because the rule willadequately describe the variability for mound-shaped distributions ofdata encountered in real life. The relative frequencies of mound-shapeddistributions are largest near the center of the distribution and tendto decrease as you move toward the distribution tails. Because the ±4Value Chart™ range closely resembles the ±1 standard deviation range andthe ±8 Value Chart™ range closely resembles the ±2 standard deviationrange on most Price Action Profiles™, these Value Chart™ ranges arediscussed instead of standard deviation measurements, but these may beused alternatively.

Referring to FIG. 15, we can observe an abundance of useful informationfrom the Price Action Profile™ analysis of the Soybeans market. Forexample, it is seen from the column to the left of the Price ActionProfile™ chart that the Soybeans Value Chart™ only trades above the +12Value Chart™ price level 0.14% of the time (see FIG. 17). It is alsoseen that the Soybeans Value Chart trades within the ±2 Value Chart™levels 37.08% of the time (see FIG. 18). Furthermore, the Soybeans ValueChart™ only trades below the −8 Value Chart™ level 2.08% of the time(see FIG. 19).

For the remainder of this description, Value Charts™ with lines locatedat the ±4 and the ±8 price Value Chart™ price levels are used. This isprimarily because these Value Chart™ price levels approximatelyrepresent the ±1 standard deviation (approximately 68% of Value Chart™price bars) and the ±2 standard deviation (approximately 95% of ValueChart™ price bars) ranges, respectively.

The Bell Curve as a Valuable Market Analysis Tool

Statistics have been a valuable tool in many different areas ofbusiness. The government is constantly releasing statistics aboutsubjects ranging from crime to average life expectancies. Manufacturingcompanies rely on statistics to monitor quality control issues in theirmanufacturing plants. The objective of statistics is to make aninference about a population or data, based on information contained ina sample. When we refer to a population in the arena of statistics, weare referring to the set of all measurements of interest. A sample mightbe defined as a subset of measurements obtained from the population.

The value of bell curves in describing a sample, which should closelyresemble the population, is that they are able to present information inthe form of a chart. Charts are simply pictures of information. Chartseffectively condense and describe information that is easilycomprehended by both the novice and the veteran. The goal of traders isto have insight into how the market is behaving at present and how themarket is likely to behave in the future. The Price Action Profile™(Bell Curve) generated from the sample datum allows inferences to bemade about the population of price datum (future price behavior).

Validating Value Charts™ with Price Action Profiles™

The key to allowing Value Charts™ to be useful over time is thevolatility adjusted Value Chart™ intervals. These volatility-adjustedintervals (dynamic volatility intervals) allow a Value Chart™ to adaptto changing market volatility and therefore remain useful in quantifyingrelative overbought and relative oversold price levels. Therefore,similar Price Action Profiles™ (or Value Chart™ distribution bellcurves) would be generated when Value Charts™ are applied to differentmarkets even though the characteristics of individual markets mightdiffer significantly.

Value Charts™ and Price Action Profile™, as market analysis tools, passtwo important tests that verify confidence in them as valid technicalanalysis tools. First, Price Action Profiles™ (bell curves) generatedfrom the Value Charts™ of several different markets are fairly similar.Second, a Price Action Profile™ generated from one decade of price datumis fairly similar to a Price Action Profile™ generated from anotherdecade of price datum for any given market. As market volatility canchange dramatically over time in any given market. The second test willverify that Value Charts™ can effectively adapt and remain useful whenmarket volatility changes over time, as is often the case.

Comparing the Price Action Profiles™ of Different Markets

Generating a Price Action Profile™ from the Value Charts™ of severaldifferent markets is performed and the results are compared. Theassumption is that the Price Action Profiles™ generated from severaldifferent markets will be very similar even though the characteristicsof each individual market may be very different. For example, theSoybeans market tends to have huge bull markets that experienceincreasing volatility until the climatic top is reached where volatilityreaches extreme levels. In addition, the Soybean market tends to havenumerous weather scares in which the prices can erupt to the upside ordownside with little notice. The Eurodollar market, on the other hand,tends to have smooth trends that reflect the long-term economic policiesof the government. Although the Eurodollar market can at times becomevolatile, it tends to maintain a calm disposition when compared to theSoybean market. In comparing Price Action Profiles™, we would expectthat the profiles from these significantly different markets to be verysimilar to the normal mound-shaped bell curve discussed in the EmpiricalRule (FIG. 16).

Generating a Price Action Profile™ for each market, all of thehistorical datum that is commonly available for each market ispreferably used. Because the futures markets have gaps between theprices of different contract months, a continuous adjusted contracts iscreated to perform the analysis. For each market under consideration, wewill generate a Price Action Profile™ and calculate the correspondingprofile characteristics as shown in the columns at the right hand sideof FIG. 15. Analyze the Price Action Profiles™ from several futuresmarkets obtained from different market sectors like the grains, foods,currencies, energies, metals, financial instruments, and stock indexesindicates there aspects of the invention. Furthermore, Price ActionProfiles™ from several popular stocks are also analyzed. Analyzing asample of vastly differing markets indicates the universal effectivenessof the Value Chart™ and Price Action Profile™ concepts. Displayed inFIG. 21, a table compares the characteristics of all of the Price ActionProfiles™ generated from the markets shown in these examples.

As the Price Action Profile™ analysis table displayed in FIG. 21 isreviewed, the similarities in the characteristics of the pricedistributions from each market are recognized. Observe the percentage ofthe Value Chart™ price bars fall between the ±4 and the ±8 Value Chart™price levels for each different market.

After reviewing FIG. 21, which represents a comparison of all of thePrice Action Profiles™ from the different markets in this comparison, itis seen that all of the bell curves for these different markets are verysimilar. These markets represent a very diverse group of markets thathave significantly different characteristics. However, their PriceAction Profiles™ are very similar. All of the Price Action Profiles™generated from these different markets have very similar standarddeviations, equal to approximately ±4.33. One standard deviation fromthe mean of a normal mound-shaped bell curve should containapproximately 68% of the sample data, which in this case is daily pricebars. Reference FIG. 21 for the following observations. The Price ActionProfiles™ in this study on average contain 69.9% of the daily ValueChart™ price bars within ±1 standard deviation from zero. Furthermore,two standard deviations from the mean of a normal mound shaped bellcurve should contain approximately 95% of the sample data. The PriceAction Profiles™ on average in this study contain 96.3% of the dailyValue Chart™ price bars within ±2 standard deviation from zero. ThePrice Action Profile™ analysis displayed in FIG. 21 confirms that ValueCharts™ are effective in adapting to significantly different marketenvironments.

Further, the Price Action Profiles™ generated from several differentmarkets were very similar. Recalling that the Price Action Profile™simply represents the distribution of the Value Chart™ price activityfor a market, the table of results displayed in FIG. 21 demonstrate thatall of these markets have very similar Value Chart™ price distributions.Therefore, regardless of the market being analyzed, Value Charts™ as amarket analysis tool is effective in identifying relative overbought,oversold, or fair value price levels. From this, a convention for howoverbought, oversold, and fair value Value Chart™ price levels (see FIG.22) can be defined.

As stated above, all Value Chart™ prices within the ±4 Value Chart™price levels will be considered fair value. This fair value rangerepresents approximately the range ±1 standard deviations from the mean,or zero. Value Chart™ prices that trade between the ±4 and +8 or −4 and−8 Value Chart™ price levels will be considered moderately overbought ormoderately oversold, respectively. These moderately overbought andmoderately oversold ranges represent approximately the range of ValueChart™ prices between ±1 standard deviations from the mean and ±2standard deviations from the mean. Value Chart™ prices that trade abovethe +8 or below the −8 Value Chart™ price levels will be consideredsignificantly overbought or significantly oversold, respectively. Thesesignificantly overbought and significantly oversold ranges representapproximately the range of Value Chart™ prices outside of ±2 standarddeviations from the mean, or zero. Now observe this convention on asample Value Chart™ in FIGS. 23 a and 23 b.

Comparing the Price Action Profiles from Two Different Decades

Another important attribute of the Value Charts™ market analysis conceptis seen in analyzing the market activity from two different decades andcomparing the Price Action Profiles™ generated from these differentperiods of time. Each time period will include approximately ten yearsworth of daily price bar datum in the examples shown. As you know,market volatility can significantly change from one decade to the other.During the 1980s the S&P 500 market traded around the 250 point level.During the 1990s the S&P 500 market traded up to the 1400 point level.Given the significantly different volatility characteristics from thesetwo different decades, Value Chart™ should be able to effectively adaptto either decade and successfully identify overbought and oversold pricelevels. The S&P 500 Price Action Profiles™ from these two differentdecades should be very similar as Value Charts™ effectively adapted tothe continually increasing volatility in the market.

The daily volatility of the S&P 500 futures market trading around the1400 point level will be significantly greater than the daily volatilityof the S&P 500 futures market trading around the 250 point level. If thePrice Action Profiles™ from these two different decades are similar,then we will reason that the dynamic volatility intervals in the ValueCharts™ are able to effectively adapt to changing volatility in themarkets.

The table found in FIG. 24 display the Price Action Profile™ analysisresults from the S&P 500 futures market during the 1980s (SP80) and the1990s (SP90). The analysis used to generate each column in the tableutilizes approximately ten years of daily bar chart price datum. Forexample, the first column in FIG. 24 labeled “SP80” displays the PriceAction Profile™ analysis from the daily S&P 500 Value Chart™ price barsrecorded in the 1980s. The next column displays the Price ActionProfile™ analysis from the daily S&P 500 Value Chart™ price barsrecorded in the 1990s. As you can see, the interval ranges representing(−4 to +4) and (−8 to +8) have bold dotted lines around them. This isbecause the ±4 on the Value Chart™ is approximately one standarddeviation from the mean and ±8 on the Value Chart™ is approximately twostandard deviations away from the mean. It is seen that the results fromthese two different decades of trading activity (FIG. 24) are similar.In the 1980s, the Value Chart™ range from −4 to +4 contained 69.5% ofthe Value Chart™ price bars while in the 1990s this same Value Chart™range contained 69.8% of the Value Chart™ price bars. Further, the factthat during 1980s and the 1990s the range from −8 to +8 contained 96.8%of the Value Chart™ bars, representing identical distributionmeasurements indicates the adaptive nature of the invention. Theseresults are achieved even though the volatility increased sodramatically during the 1990s in the S&P 500 futures market.

It is possible for the Price Action Profiles™ from two different decadesin a given market to differ more significantly than the Price ActionProfiles™ did from the S&P 500 example. For example, the Cocoa market inthe 1970s experienced an explosive decade long bull market. However,Cocoa in the 1980s and the 1990s experienced a prolonged bear market(see FIG. 25).

We would expect the Cocoa Price Action Profile™ from the 1970s,generated from daily Cocoa Value Charts™, to be skewed so that more than50% of the profile would appear in the positive Value Chart™ intervals.In the 1980s and the 1990s the profile should appear to be more normalin appearance. Often differences in the Price Action Profiles™ from twodifferent time periods can be explained by major prolonged bull or bearmarkets. When a Price Action Profile™ is generated from a long enoughtime period that is inclusive of up and down market cycles, it shouldappear very similar to those displayed in FIGS. 15 and 20.

As previously indicated, any type of price datum, including, but notlimited to, tick charts, bar charts, candle stick charts, point & figurecharts, any type of price charts, technical charts and chartingindicators, can be converted to Value Charts™. Traditional bar chartstypically include the Open, High, Low, and Close prices (with respect tozero). Value Charts™ plot the Open, High, Low, and Close prices withrespect to a defined “floating axis”.

The floating axis can be defined as any function of price. The distancethat price lies away from the floating axis is directly related to thedegree of buying and selling that has come into the market at that time.We will designate F as the function that generates our floating axisvalues. The function (F) is user defined and can be any function ofprice. In our example, F will be defined as a five-day moving average ofthe median price in each of the five latest price bars. Value Charts™can be easily customized because we have the capability to use anyfunction of price that we want for the floating axis. However, thesensitivity to price change that the user desires will dictate how manyprice bars (or price data points) will be taken into consideration inthe calculation. The ability to customize the function for the floatingaxis gives the user the power to tailor PAP's and VC's to his ownspecifications. It should be noted, however, that certain functions of Fproduce better Price Action Profiles™, or bell curves, from the ValueChart™ price behavior.

Exemplary Floating Axis Calculation

M=Median Price=(H+L)/2

F=Floating Axis Function=(M+M[1]+M[2]+M[3]+M[4])/5

Brackets denote number of days ago: [Number of days ago]

-   -   Example: M[1]=Median price from one day ago (yesterday)

Once the floating axis has been defined, we must define an interval torepresent the unit value on the y-axis (dynamic volatility unit). Thisdynamic volatility unit will be used to define the point value for theValue Chart™. This interval can be any function of price. However, thesedynamic intervals should be designed to expand and contract along withchanging market volatility. We will denote our interval function asfunction (DVU). Like the function F for the floating axis, the functionDVU is user defined and can be any function of price. For our example,we will define the DVU as a function that generates a dynamic volatilityunit by taking a five-day moving average of a bar's trading range (H-L)or today's close minus yesterday's close, whichever is greater, and thendividing this value by 5. DVUs give Value Charts™ the ability to adaptto changing market volatility. This allows Value Charts™ and PriceAction Profiles™ to be effective in defining overbought and oversoldprice levels in changing volatility conditions.

Exemplary Dynamic Volatility Unit Calculation

R=(Price Range)=(H−L) or (C-C[1]) {The greater of the two values}

A=Average 5-Day Price Range=(R+R[1]+R[2]+R[3]+R[4])/5

DVU=Dynamic Volatility Unit=A/5

Brackets denote number of days ago: [Number of days ago]

-   -   Example: R[1]=Price Range one day ago (yesterday)

Value Charts™ will assume that the x-axis will reflect time. The y-axison the Value Charts will be defined in terms of volatility units. Thex-axis on Price Action Profiles™ will be defined in terms of DVUs. They-axis of Price Action Profiles™ will be defined as a percentage of therelative frequency of occurrences of Value Chart™ price bars containedin each corresponding dynamic volatility unit.

Now that the floating axis function (F) and the dynamic volatility unitfunction (DVU) are defined, a Value Chart™ and Price Action Profile™ canbe generated. It is important to note that Value Charts™ can plot anyprice chart. Price Action Profiles™ simply represent the distribution ofValue Chart™ prices across the dynamic volatility unit range. For ourexamples we will use traditional bar charts to create the Value Charts™(VCs) and Price Action Profiles™ (PAPs). The next step in creating a VCand a PAP is to convert the traditional price values of the Open, High,Low, and Close from their absolute values to their corresponding ValueChart™ values. Again, there also is the ability to take tick datum,candlestick charts, or any price datum, and generate Value Charts™ andPrice Action Profiles™. VCs can be charted as bar charts, tick charts,candle stick charts, or any other price representation that mirrors (ifdesired) the traditional chart being transposed. The major unit value onthe x-axis of the Value Chart™ will be the designated time interval ofthe traditional price or bar chart being converted as defined by theuser (i.e. Weekly, Daily, 5-Minute, etc.). VCs can be plotted eitherwith (preferable) or without the traditional chart that it is beingcalculated from. Typically, when viewing a bar chart, if you move oneunit to the right or left on the x-axis (time axis) of a Value Chart™,you will move to the same corresponding time interval on the traditionalchart that will preferably be displayed above the VC. The major units onthe y-axis will be equal to one unit as defined by the dynamicvolatility unit function (DVU). We will convert the bars (prices datum)as follows:

Exemplary Calculation of Value Chart™ Prices with Dynamic VolatilityIntervals

VCopen=Value Chart Open=(Open-F)/DVU

VChigh=Value Chart High=(High-F)/DVU

VClow=Value Chart Low=(Low-F)/DVU

VCclose=Value Chart Close=(Close-F)/DVU

VCprice=Value Chart Price=(Price-Floating Axis)/(Dynamic VolatilityUnit)

Once the absolute price datum has been converted to Value Chart™ prices,the Value Chart™ can be generated by simply plotting the values on achart with time reflected on the x-axis and Value Chart price reflectedon the y-axis, which is defined in terms of dynamic volatility units.

Finally, in creating a Price Action Profile™, it is determined how oftenthe Value Chart™ prices trades in each dynamic volatility unit interval.In other words, we need to determine the frequency that the Value Chart™price has traded in each dynamic volatility unit range (sector.) Once weknow the frequency that Value Chart™ price has traded in each sector, wecan create a Price Action Profile™. Calculating a frequency diagram fromtick datum simply involves summing up all of the tick values for eachsector and, if desired, dividing that sum by the total number of ticksto give a percentage of price ticks in each sector or a relativefrequency histogram. For these examples, price bars are used. We willnot concern ourselves with the distribution of trading activity withineach individual price bar, although this can be taken into considerationfor more advanced Price Action Profile™ studies.

As illustrated in FIG. 31, there are six different scenarios to considerwhen determining what percentage of a price bar has traded in each DVUinterval range. We will define the area between two interval lines onthe y-axis as a Sector (Dynamic Volatility Unit Interval Range) The sixscenarios are listed below:

1) Bar located above sector:

2) Bar located below sector:

3) Bar passes all of the way through sector:

4) Entire bar is inside of sector:

5) High of bar is in sector and low of bar is below sector:

6) Low of bar is in sector and high of bar is above sector:

Using If-Then logic, we will determine what percentage of each bar liesin each corresponding sector. If we were analyzing tick data points, wewould simply count how many tick data points occurred within eachsector.

N^(P)=Percentage of the bar that is located in the P sector.

P=Sector Number (i.e. P=1 is the 1^(st) sector between 0 and +1)

A=Top interval value of sector.

B=Bottom interval value of sector.

A-B=Sector range or Dynamic Volatility Unit Interval (or major unit)

VCP=Value Chart price (i.e. VCL=Value Chart low, VCH=Value Chart high)

1) If VCLow>A then N^(p)=0.

2) If VCHigh<B then N^(p)=0.

3) If VCHigh>A and VCLow<B then N^(p)=(A−B)/(VCH−VCL)

4) If VCHigh<A and VCLow>B then NP=100%

5) If VCHigh>B and VCLow<B then NP=(VCH−B)/(VCH−VCL)

6) If VCLow<A and VCHigh>A then NP=(A−VCL)/(VCH−VCL)

Once we are finished determining where each bar is located (or dividedacross the sectors), then we can create a Price Action Profile™ bellcurve (frequency diagram.) The Price Action Profile can be displayed asa smooth bell curve, frequency histogram, relative frequency histogram,etc. To determine what percentage of trading occurred in each ValueChart™ sector, we simply take the sum of all of the partial or completetrading ranges Σ(N^(p)) that traded in that particular sector and divideby the total number of bars.

Percentage of trading in a sector=ΣN^(p)/(total # Bars)

Once we calculate the percentage of trading in each sector then we cancreate a Price Action Profile™ bell curve (frequency diagram.) (See FIG.23 b for example diagram.) Note that we will position the Price ActionProfile™ (displayed in FIG. 23 b) so that the histogram sectors arealigned with the Value Chart™ dynamic volatility unit intervals.

When a Price Action Profile™ is generated from the price datum of anystock, futures, or bonds, markets (etc.) and from the formulas displayedabove, the resulting bell curve displays statistically valid bell curveattributes. Most importantly, the bell curves generally containapproximately 68% of the price distribution within one standarddeviation and approximately 95% of the price distribution within twostandard deviations (See Empirical Rule in FIG. 16). If these profilesof Value Chart™ price activity were generated on a non-volatilityadjusted basis, the resulting profile would resemble a thorn shapedprofile (See FIG. 13 b) and would therefore have little practicalapplication. However, the volatility adjusted price intervals create astatistically valid bell curve that can be used to make statisticalinferences on future market activity (see FIG. 14 b).

Practical Application

There are numerous potential applications of the Value Charts™ and PriceAction Profile™ concepts. These concepts can be used eitherindependently or in conjunction with a traditional bar or price chart orother market indicators and charting techniques. Listed below areseveral example applications of these innovative new concepts.

Identifying Low Risk Exposure Market Entry (or Exit) Points Using ValueCharts™

Now that the creation of Value Charts™ and Price Action Profiles™ isunderstood, different examples of how these market analysis tools can beapplied to the markets are described. One of the most powerfulapplications of the Value Charts™ and Price Action Profile™ conceptslies in the ability to identify optimal entry points in a trendingmarket (see FIG. 26). Upon examining a S&P 500 Price Action Profile™, weobserve that the S&P 500 futures market only trades below the −6 ValueChart™ price level 4.87% of the time. Amazingly, nine out of seventeenbuy opportunities that were identified in FIG. 26 had no risk exposureto loss for the 100 days plus following each buy signal. The other eightbuy opportunities identified in FIG. 26 experienced minimal riskexposure during the several days following each buy signal. The −6 ValueChart™ price level was selected because it appeared to work well inidentifying low risk buy points in several past S&P 500 bull markets. Itis important to note that the S&P 500 bull market example in FIG. 26represented the ideal conditions for this particular trading strategy.

Now that we have demonstrated that Value Charts™ has the ability toidentify low risk exposure market entry points, we can view the averageworst exposure profitably graph (FIG. 27) generated from all seventeenbuy points in FIG. 26. FIG. 27 simply subtracts the Value Chart™ entrybuy price from the low price of the 100 days following each market entrypoint. All seventeen profitability graphs are then averaged to form FIG.27. Note that Value Charts™ allowed us identify seventeen low riskexposure buy points, as verified by the profitability graph in FIG. 27.Although the S&P market was in a strong bull market during the 1995calendar year, Value Charts™ was very successful in identifying manyoptimal buy points in this particular bull market.

The average worst exposure profitability graph in FIG. 27 represents theaverage profitability of each of the seventeen buy points from FIG. 26for the 100 days following each buy signal. Incredibly, within threedays from each point where the S&P 500 futures traded below the −6 ValueChart™ price level, the average trade was profitable. The buy signals inFIG. 26 are as close to the perfect buy signals as one would hope toattain. FIG. 26 also serves as an example of how traditional bar chartbars can be “flagged” with a dot (or arrow, etc.) when certain ValueChart™ price levels are penetrated.

The buy signal displayed in FIG. 28, generated from trading activitythat penetrated below the −8 Value Chart™ price level on an AmericanExpress daily chart, included the bottom $0.50 portion of the AmericanExpress price bar on Jun. 24, 1999. The maximum exposure to loss was a$0.50 short-lived losing period between the entry level ($119.635) onJun. 24, 1999, and the low of the same trading day ($119.125). Thisposition became immediately profitable after the signal day and soongenerated profits of $18.25 per share as American Express closed sevendays later (on Jul. 6, 1999) at a price level of $138.25. Although thetrading signal displayed in FIG. 28 was an ideal scenario, itnevertheless exemplified the potential that Value Charts™ and PriceAction Profile™ have to identify low risk exposure market entry pricepoints. These powerful market analysis tools can also be used toidentify market exit points also. Value Charts™ and Price ActionProfile™ can be used in conjunction with any other effective marketanalysis tools in order to develop powerful trading strategies.

Designing Trading Systems with Value Charts™

The buy and sell signals in FIG. 29 a, which displays daily price barsfrom the Soybeans futures market, were generated from a stop-and-reversetrading system. As seen, the buy and sell signals in FIG. 29 a wereinitiated at undesirable price levels. An analysis of these trades canbe found in the table displayed in FIG. 30.

The trend-following system displayed in FIG. 29 a performed poorlyduring the choppy Soybeans market that unfolded during the summer of1999. Trend-following systems often incur losses during choppy marketconditions. With the development of Value Charts™, there is now theability to quantify relative price levels that can potentially improvethe performance of trading systems during choppy climates like these.The trading signals generated from the enhanced trend-following system(displayed in FIG. 29 b), which utilizes the power of Value Charts™ andPrice Action Profile™, were significantly improved with better entry andexit price levels over the normal trading system signals. Instead ofacting on the buy and sell signals generated from the trend-followingsystem, we waited until the Value Chart™ price penetrated the −6 ValueChart™ price level for buy signals and the +6 Value Chart™ price levelfor sell signals. Thus; original trading system was used as a filter andused Value Chart™ price levels to enter and exit the Soybeans market. Aswe can see from the table listed in FIG. 30, the total savings realizedfrom the improved entry prices was $5,100 per contract. Both of thenormal buy signals listed in the top half of the table in FIG. 30 weresignaled at significantly overbought relative price levels. This isevident by the fact that the Soybeans market has historically traded ator above these overbought relative price levels only 1.37% of the time.The normal sell signal was generated at a moderately oversold ValueChart™ price level in which the Soybeans market only trades at or below6.78% of the time.

The user can determine when to activate the Value Chart™ enhancedversion of the trading program. There is also discretion in determiningthe Value Chart™ price level at which a trader will enter a choppymarket. The present invention provides a powerful tool that can beincorporated into any trading approach. The concepts can be used to exittrading positions at profit targets and pyramid into large positions.There are literally thousands of different examples of how thesepowerful concepts can be incorporated into trading approaches andtrading systems, and the invention contemplates and allows integrationinto any such system.

Tracking the Overbought or Oversold Condition of Hundreds of Markets

Utilizing Value Charts™ and Price Action Profile™, we now have theability to define relative value for any market. We now have the abilityto scan through literally thousands of stocks, bonds, futures, etc.markets to identify significantly overbought or significantly oversoldmarkets (see FIGS. 22 and 23 b). Having the ability to define relativevalue allows us to write programs that trigger alarms or alerts when anymarket reaches an overbought, oversold, or fair value price level. Wecan then use this capability to track and trade in more markets. Once wereceive an alarm signaling, for example, that a market that we want tobuy has just penetrated an oversold (undervalued) Value Chart pricelevel, we can move in and enter the market with precision.

Although there are many potential applications of this concept, severaladditional practical applications are described below:

-   1) An elementary application for any investor will be to simply use    the Value Bars™ in conjunction with the Price Action Profile™ to    quickly and easily identify statistically attractive buying and    selling price levels. These charts are powerful in that they give an    investor the ability to rapidly process (at a glance) relative value    in a market at the current price level. PAPs and VCs can identify    overbought, oversold, or fair value with changing market volatility.    Traditional bar charts do not in themselves give investors the    ability to do this. Even the novice, who knows virtually nothing    about statistics, can utilize this information by observing how    overbought or oversold a particular investment is. Also, these    charts can be used in determining where not to buy. Often investors    get caught up in chasing overextended markets where they buy at    overbought or sell at oversold price levels. These trades often    result in high risk exposure trades. When the inevitable price    correction comes, they get driven out of the market with a loss even    though they may be right about the long-term direction of the    market. Depending on the size of the investment, this information    might result in the savings of thousands, even millions of dollars.-   2) Both the Value Charts™ and the Price Action Profile™ can be    easily customized. For example, an investor can choose to customize    any one of the following setups (and many more):    -   Floating Axis=any function that involves price or price and        time.    -   Dynamic Volatility Units=Dynamic Volatility Units can use any        function of price or price and time.    -   Time Period=Bar charts, candle stick charts, etc. can be        customized to any time frames (monthly, weekly, daily,        60-minute, etc.) This makes Value Charts™ very flexible.        -   It should be noted that this concept works better on some            time frames versus other time frames. The Price Action            Profile™ should confirm the validity or lack of validity for            every customized Value Chart™.-   3) When evaluating a daily Value Chart™, for example, the investor    can display both the weekly Price Action Profile™ at the left end of    the screen and display a daily bar chart on the weekly Value Chart    axes. Users therefore can set the PAP to a weekly (or longer time    period) chart and the VC to a weekly chart, which would be plotted    on the weekly scale. A daily Value Chart™ can then be plotted on the    weekly Value Chart™ intervals. This allows investors to track daily    Value Bars™ on a weekly scale (or against a weekly bell curve.)-   4) Many trading system developers have traditionally used the open    and close of the day to generate trading signals because these    prices were the only basic quantifiable price datum that have been    available to drive trading systems. Now, in addition to using the    open and close of the day, system developers can generate buy and    sell commands from quantifiable Value Chart™ price levels. This    ability to quantify relative overbought, oversold, or fair value    price levels literally allows trading system developers to    significantly expand the set of quantifiable values that can be used    to drive a systematic trading system. For example, a trading system    designer might type “If Value Chart price trades at or below −2 then    Buy 1 contract at the market. Exit the long position when the Value    Chart price trades above +3”-   5) Many time investors have a good fundamental picture of where a    market is going to trend. However, they have not had the tools to    buy or sell at attractive low risk exposure price levels. Now, a    bullish fundamental trader can accumulate a stock when the Value    Chart™ gets oversold (undervalued) or simply reaches fair value.    This new strategic market entry strategy would prove to get the    investor in the market at much more desirable price levels than an    arbitrary market entry approach. They can now identify low risk    exposure market entry and exit points. Traders can complement VCs    and PAPs with a momentum indicator for the purpose of identifying    key market entry or exit price levels.-   6) Value Chart™ price levels can be used to customize the appearance    of any traditional bar chart or price chart. For example, normal or    traditional bar chart sections (one bar can have different colors if    it trades across several Value Chart™ price levels) or price chart    values can be displayed with different colors corresponding to the    different overbought, oversold, or fair value price levels on the    Value Chart™. These colors can be defined by the user. Bar charts or    price charts can be colored different colors corresponding to how    may standard deviations they trade away from the floating axis or    mean. Traditional price bars (or price in any form) can be “flagged”    when certain conditions are met.-   7) The Price Action Profile™ can be customized to reflect the    distribution of price, with respect to any user defined floating    axis, for any set of price datum. This set of datum could simply    reflect the datum used to chart the traditional bar or price being    analyzed or reviewed. If the price datum of the market being    reviewed is limited, the Price Action Profile™ can access a file    with more extensive price history of the market being analyzed and    use this more extensive data to generate more extensive Value Chart™    history, and then a more accurate Price Action Profile™ (bell curve    or frequency diagram). The user may use customized formula (or    condition) to define when to collect price datum for the Price    Action Profile™. For example, a user could define a condition that    only collected price datum when the market price or price bar was    trading above the 30-Day moving average of the closing price for    that market and thus deemed to be in a bull or rising market. These    Conditional Price Action Profiles™ are useful in defining the    characteristics of a market when in a bull market, for example, as    defined by a user defined bull market condition.-   8) Users can place a Performance Stop™ in the market for the purpose    of exiting their position if their expectations are not met. For    example, “If Value Chart™ price does not exceed +5 before 5 days    after the market entry date then exit the long position at the    close.” In other words, traders can track Value Chart™ price to    confirm that their expectations were being met.-   9) With the power of computers to track literally hundreds of    stocks, bonds, or futures markets at a time, Price Action Profile™    and Value Charts™ can be used to track the overbought or oversold    level of every market that an investor is interested in tracking. A    mutual fund manager can track the average overbought or oversold    level of every stock that he or she trades and compare this to the    overbought or oversold level of any stock index for any given day,    week, month, etc. Another example could include the case where an    investor has his or her computer sound an alarm if a particular    market reaches a particular Value Chart™ price level.-   10) An investor can track the overbought or oversold state of a    market across several timeframes. For example, in FIG. 38 a the    daily Value Chart™ is displayed for Coca Cola and in FIG. 38 b the    monthly Value Chart™ is displayed for Coca Cola. Both charts end on    Oct. 8, 1999. FIG. 39 displays the Price Action Profiles™ for both    for both of the Value Charts™ displayed in FIGS. 38 a and 38 b. The    daily Coca Cola chart closed at the 6.67 Value Chart™ price level    while the monthly Coca Cola chart closed at the −3.55 Value Chart™    price level. As you can see, on Oct. 8, 1999, Coca Cola was    moderately overbought on a daily basis and slightly oversold on a    monthly basis. Value Charts™ and Price Action Profiles™ from several    different time frames can be displayed in any combination on a    single screen or display.-   11) A screen, or Time Valuation Grid, can be displayed for a market    (FIGS. 40 and 41) to show the most recent Value Chart price with    respect to the Price Action Profiles from several different time    frames in the same market (5-minute, 30-minute, daily, weekly,    etc.). This would give an investor the ability to quantify the    overbought or oversold level of a market across several different    time frames.-   12) A screen, or Market Valuation Grid, can be displayed for several    markets (FIGS. 42 and 43) to show the most recent Value Chart price,    for each market, with respect to the Price Action Profiles from each    market under consideration. This would give an investor the ability    to quantify the overbought or oversold levels of several different    markets at the same time. The same or different time frames can be    displayed for the markets. The Market Valuation Grid and the Time    Valuation Grids can be combined to form a single grid if desired.-   13) Value Chart prices can be transposed back to an absolute basis    to form a volatility adjusted bar chart (see FIG. 44). This can be    accomplished by simply adding the new Value Chart price bar values    to the previous day's volatility adjusted (or transposed) close.    Using the Value Chart™ formulas, a volatility adjusted absolute    chart can be generated by simply adding the Value Chart™ open, high,    low, and close prices to the previous Value Chart™ close. In other    words, instead of plotting Value Chart™ prices with respect to the    floating axis, this new volatility adjusted absolute chart at the    bottom of FIG. 44 adds the next price bar (price) to the previous    bars close.

The current valuation of the crude oil market over different time framesis shown in FIG. 40. The arrows display how overbought or oversold CrudeOil is on, for example, the daily timeframe (see arrow under “daily”column). In this case, Crude Oil trades above this overbought state12.0% of the time and below this overbought state 88% of the time. Thefair value section, as defined by plus or minus one standard deviation,is colored green, the moderately overbought and oversold section, asdefined by plus or minus one standard deviations to plus or minus twostandard deviations, is colored yellow, and the significantly overboughtand oversold section, defined as beyond plus or minus two standarddeviations, is colored red. The momentum arrows at the bottom of eachcolumn indicate increasing upward momentum (up arrow colored green),decreasing upward momentum (up arrow colored yellow), increasingdownward momentum (down arrow colored red), and decreasing downwardmomentum (down arrow colored yellow).

The current valuation of the crude oil market over different time framesis shown in FIG. 41. The arrows display how overbought or oversold CrudeOil is on, for example, the daily timeframe (see arrow under “daily”column). In this case, Crude Oil trades above this overbought state12.0% of the time and below this overbought state 88% of the time. Thefair value section, as defined by plus or minus one standard deviation,is colored light green, the moderately overbought and oversold section,as defined by plus or minus one standard deviations to plus or minus twostandard deviations, is colored green, and the significantly overboughtand oversold section, defined as beyond plus or minus two standarddeviations, is colored dark green. The momentum arrows located in eachcolumn represent valuation of each time period with respect to thecorresponding Price Action Profile™ and, in addition, indicateincreasing upward momentum (up arrow colored green), decreasing upwardmomentum (up arrow colored yellow), increasing downward momentum (downarrow colored red), and decreasing downward momentum (down arrow coloredyellow).

The current valuation of the crude oil market over different time frameis shown in FIG. 42. The arrows display how overbought or oversold thedifferent markets are and show momentum for each daily market. Forexample, Soybeans trades above the current overbought state 12.0% of thetime and below the current overbought state 88% of the time. The fairvalue section, as defined by plus or minus one standard deviation, iscolored green, the moderately overbought and oversold section, asdefined by plus or minus one standard deviations to plus or minus twostandard deviations, is colored yellow, and the significantly overboughtand oversold section, defined as beyond plus or minus two standarddeviations, is colored red. The momentum arrows at the bottom of eachcolumn indicate increasing upward momentum (up arrow colored green),decreasing upward momentum (up arrow colored yellow), increasingdownward momentum (down arrow colored red), and decreasing downwardmomentum (down arrow colored yellow).

The current valuation of the crude oil market over different time framesis shown in FIG. 43. The arrows display how overbought or oversold thedifferent markets are and show momentum for each daily market. Forexample, Soybeans trades above the current overbought state 12.0% of thetime and below the current overbought state 88% of the time. The fairvalue section, as defined by plus or minus one standard deviation, iscolored light green, the moderately overbought and oversold section, asdefined by plus or minus one standard deviations to plus or minus twostandard deviations, is colored green, and the significantly overboughtand oversold section, defined as beyond plus or minus two standarddeviations, is colored dark green. The momentum arrows located in eachcolumn represent the valuation of each market with respect to thecorresponding Price Action Profile™ and, in addition, indicateincreasing upward momentum (up arrow colored green), decreasing upwardmomentum (up arrow colored yellow), increasing downward momentum (downarrow colored red), and decreasing downward momentum (down arrow coloredyellow).

As the potential applications of Price Action Profile™ and Value Charts™are unlimited, the scope of the invention is contemplated to include allsuch applications. The potential applications of Value Chart™ and thePrice Action Profile are almost endless because these chartingapplications are easily customizable and these charting applications canquantify relative value for any market participant. Furthermore, thesepowerful charting techniques can relay real-time market information at aglance and therefore do not require the investor to expend much energyin deciphering or interpreting text data or indicator values.

Formulas Usable for the Floating Axis and Y-Axis Volatility Intervals

1) Vchart (Function)—This Function returns Value Chart prices.

Inputs: NumBars(Numeric), Price(NumericSeries);

Variables:VarNumBars(0),Var0(0),LRange(0),YDiv(0),RanVar4(0),VOpen(0),VHigh(0),VLow(0),VClose(0),

VarA(0),VarB(0),VarC(0),VarD(0),VarE(0),VarP(0),VarR1(0),VarR2(0),VarR3(0),VarR4(0),VarR5 (0);

{Insure NumBars is between 2 and 1000}

If NumBars<2 then VarNumBars=2;

If Numbars>1000 then VarNumBars=1000;

If Numbars>=2 and NumBars<=1000 then VarNumBars=NumBars;

VarP=Round(VarNumBars/5,0);

If VarNumBars>7 then begin

VarA=Highest(H,VarP)-Lowest(L,VarP);

If VarA=0 and VarP=1 then VarR1=absvalue(C-C[VarP]) Else VarR1=VarA;

VarB=Highest(H,VarP)[VarP+1]-Lowest(L,VarP)[VarP];

If VarB=0 and VarP=1 then VarR2=absvalue(C[VarP]-C[VarP*2]) ElseVarR2=VarB;

VarC=Highest(H,VarP)[VarP*2]-Lowest(L,VarP)[VarP*2];

If VarC=0 and VarP=1 then VarR3=absvalue(C[VarP*2]-C[VarP*3]) ElseVarR3=VarC;

VarD=Highest(H,VarP)[VarP*3]-Lowest(L,VarP)[VarP*3];

If VarD=0 and VarP=1 then VarR4=absvalue(C[VarP*3]-C[VarP*4]) ElseVarR4=VarD;

VarE=Highest(H,VarP)[VarP*4]-Lowest(L,VarP)[VarP*4];

If VarE=0 and VarP=1 then VarR5=absvalue(C[VarP*4]-C[VarP*5]) ElseVarR5=VarE;

LRange=((VarR1+VarR2+VarR3+VarR4+VarR5)/5)*0.2;

End;

If VarNumBars <=7 then Begin

If AbsValue(C-C[1])>(H−L) then Var0=AbsValue(C-C[1]) else var0=(H−L);

If H=L then Var0=absvalue(C-C[1]);

LRange=Average(Var0,5)*0.2;

End;

If Price=Open then

VChart=((Open-Average((H+L)/2,VarNumBars)))/(LRange);

If Price=High then

VChart=((High-Average((H+L)/2,VarNumBars)))/(LRange);

If Price=Low then

VChart=((Low-Average((H+L)/2,VarNumBars)))/(LRange);

If Price=Close then

VChart=((Close-Average((H+L)/2,VarNumBars)))/(LRange);

2) Value Chart (Indicator)—This Indicator plots Value Chart prices.

Inputs: NumBars(5);

Variables: Vopen(0),VHigh(0),VLow(0),VClose(0);

{Calculate Value Chart}

VOpen=VChart(NumBars,Open);

VHigh=VChart(NumBars,High);

VLow=VChart(NumBars,Low);

VClose=VChart(NumBars,Close);

{Plot Value Chart—Disregards the first 20 bars because Omega doesn'tprocess them correctly}

If BarNumber>Numbars then Begin

Plot1(VOpen, “Vopen”);

Plot2(VHigh, “Vhigh”);

Plot3(VLow, “VLow”);

Plot4(VClose, “Vclose”);

End;

If BarNumber>Numbars then Begin

Plot1(VOpen, “Vopen”);

Plot2(VHigh, “Vhigh”);

Plot3(VLow, “VLow”);

Plot4(VClose, “Vclose”);

End;

3) Price Action Profile (Indicator)—This Indicator plots a Price ActionProfile of Value Chart Prices

Inputs NumBars(5);

Variables:VarNumBars(0),Var1(0),Var2(0),Var3(0),Vopen(0),VHigh(0),VLow(0),Vclose(0);

Arrays:Dist[50](0);

{Calculate Value Chart}

VOpen=VChart(NumBars,Open);

VHigh=VChart(NumBars,High);

VLow=VChart(NumBars,Low);

VClose=VChart(NumBars,Close);

{Calculate Price Action Profile}

For Var1=−25 To 25 Begin

Var2=Var1+25;

If Var1<0 then begin

If Vhigh<(Var1+1) and VLow>(Var1) then Dist[Var2]=Dist[Var2][1]+1;

If VHigh>(Var1+1) and VLow<(Var1) thenDist[Var2]=Dist[Var2][1]+((1)/(VHigh-Vlow));

If VHigh<Var1 or VLow>(Var1+1) then Dist[Var2]=Dist[Var2][1];

If VHigh<(Var1+1) and VHigh>Var1 and VLow<Var1 thenDist[Var2]=Dist[Var2][1]+((Vhigh-var1)/(Vhigh-VLow));

-   -   If VHigh>(Var1+1) and VLow>Var1 and VLow<(Var1+1) then        Dist[Var2]=Dist[Var2][1]+(((Var1+1)−VLow)/(Vhigh-VLow));

End;

If Var1>0 then begin

-   -   If Vhigh<(Var1) and Vlow>(Var1-1) then        Dist[Var2]=Dist[Var2][1]+1;    -   If Vhigh>(Var1) and VLow<(Var1-1) then        Dist[Var2]=Dist[Var2][1]+((1)/(VHigh-VLow));    -   If VHigh<(Var1-1) or VLow>(Var1) then Dist[Var2]=Dist[Var2][1];    -   If VHigh<(Var1) and Vhigh>(Var1-1) and VLow<(Var1-1) then        Dist[Var2]=Dist[Var2][1]+((Vhigh-(var1-1))/(Vhigh-VLow));    -   If VHigh>(Var1) and VLow>(Var1-1) and VLow<(Var1) then        Dist[Var2]=Dist[Var2][1]+(((Var1)-VLow)/(Vhigh-VLow));

End;

If Var1=0 then Dist[Var2]=0;

End;

Var3=Var3[1]+1;

{Print data to file}

IF LastBarOnChart=true THEN BEGIN

For Var1=−25 To 25 Begin

-   -   Var2=Var1+25;    -   If Var1< >0 then Dist[Var2]=((Dist[Var2])/Var3);    -   If Var1=0 then Dist[Var2]=0;    -   If Var1< >0 then begin    -   PRINT(FILE(“C:\PAP\us5w”),    -   Var1: 4:0,”,”,    -   Dist[Var2]: 8: 7);    -   End;

End;

END;

4) VCLevel (Function)—This Function returns Value Chart price levels.

Inputs: NumBars(Numeric),VLeveI(Numeric);

Variables:VarNumBars(0),Var0(0),LRange(0),YDiv(0),RanVar4(0),VOpen(0),VHigh(0),VLow(0),VClose(0),

VarA(0),VarB(0),VarC(0),VarD(0),VarE(0),VarP(0),VarR1(0),VarR2(0),VarR3(0),VarR4(0),VarR5(0);

{Insure NumBars is between 2 and 1000}

If NumBars<2 then VarNumBars=2;

If Numbars>1000 then VarNumBars=1000;

If Numbars>=2 and NumBars<=1000 then VarNumBars=NumBars;

VarP=Round(VarNumBars/5,0);

If VarNumBars>7 then begin

VarA=Highest(H,VarP)-Lowest(L,VarP);

If VarA=0 and VarP=1 then VarR1=absvalue(C-C[VarP]) Else VarR1=VarA;

VarB=Highest(H,VarP)[VarP+1]-Lowest(L,VarP)[VarP];

If VarB=0 and VarP=1 then VarR2=absvalue(C[VarP]-C[VarP*2]) ElseVarR2=VarB;

VarC=Highest(H,VarP)[VarP*2]-Lowest(L,VarP)[VarP*2];

If VarC=0 and VarP=1 then VarR3=absvalue(C[VarP*2]-C[VarP*3]) ElseVarR3=VarC;

VarD=Highest(H,VarP)[VarP*3]-Lowest(L,VarP)[VarP*3];

If VarD=0 and VarP=1 then VarR4=absvalue(C[VarP*3]-C[VarP*4]) ElseVarR4=VarD;

VarE=Highest(H,VarP)[VarP*4]-Lowest(L,VarP)[VarP*4];

If VarE=0 and VarP=1 then VarR5=absvalue(C[VarP*4]-C[VarP*5]) ElseVarR5=VarE;

LRange=((VarR1+VarR2+VarR3+VarR4+VarR5)/5)*0.2;

End;

If VarNumBars<=7 then Begin

If AbsValue(C-C[1])>(H−L) then Var0=AbsValue(C-C[1]) else var0=(H−L);

If H=L then Var0=absvalue(C-C[1]);

LRange=Average(Var0,5)*0.2;

End;

VCLevel=Average((H+L)/2,VarNumBars)+((LRange)*VLevel);

5) VCLeveI (Indicator)—This Indicator plots Value Price level on thetraditional bar chart.

Inputs:RSILeng(20),UpBand(56),LowBand(44),NumBars(5),BuyLevel(−2),SellLevel(2);

Variables: Var1 (−1),Var2(0),Var3(1),Var4(0),Var5(0),Var6(0),Var7(0),Var8(0), Var9(−1),Var10(1);

If RSI(Close,RSILeng)>UpBand and RSI(Close,RSILeng)[1]<UpBand andVar1=−1 Then Var1=1;

If RSI(Close,RSILeng)<LowBand and RSI(Close,RSILeng)[1]>LowBand andVar1=0 Then Var1=−1;

Var2=VCLevel(Numbars,BuyLevel);

If Var1=1 and Var1[1]=−1 then Var5=C;

If Var1=1 and L<Var2 and H>Var2 then Var6=(Var5-Var2) else Var6=0;

If Var1=1 and H<Var2 then Var6=(Var5-O) else Var6=O;

If Var1=1 and L<Var2 then Var1=0;

If RSI(Close,RSILeng)>UpBand and RSI(Close,RSILeng)[1]<UpBand and Var3=0Then Var3=1;

If RSI(Close,RSILeng)<LowBand and RSI(Close,RSILeng)[1]>LowBand andVar3=1 Then Var3=−1;

Var4=VCLevel(Numbars,SellLevel);

If Var3=−1 and Var3[1]=1 then Var7=C;

If Var3=−1 and L<Var4 and H>Var4 then Var8=(Var4-Var7) else Var8=0;

If Var3=−1 and L>Var4 then Var8=(O-Var7) else Var8=0;

If Var3=−1 and H>Var4 then Var3=0;

Plot1(Var4);

Plot2(Var2);

{TODO 3 of 3 Replace “Alert Description” with a short description of thealert}

If Condition 1 then

Alert(“Alert Description”);

In the preferred embodiment, the Price Action Profiles™ and Value Chartswill be displayed on a screen, most likely the screen of a computerworkstation. The computer will have a CPU and a communication devicethat allows it to retrieve the price datum and process and display thedatum in the Price Action Profile™ and Value Chart™ formats. Thecommunication device will preferably a CD-ROM drive or disk drive in thecomputer that allows the computer to access large amounts of historicaldatum and/or a communication device that allows the computer to accessreal-time datum broadcasted through a cable, modem, wire or received bya satellite dish or radio antenna. If the computer is accessing theValue Chart™ and Price Action Profile™ concept through the internet, thecomputer will be attached to a cable or telephone line for the purposeof sending and receiving information, charts, and any other information,alarm, etc. relating to this concept.

The CPU will process the price datum and display the price datum asPrice Action Profile™ and Value Charts™. The computer will also allowthe user to test trading systems using historical datum and Value Chart™and Price Action Profile™ quantifiable values to determine how a tradingsystem would have performed hypothetically in the past. VC and PAPquantifiable price levels allow a trading system designed to expand theset of quantifiable price values that can drive trading beyond thetraditional open and close of the present day or high and low values ofthe past. Computer software will allow the user to optimize his or herresults by testing a range of VC and PAP values and generating profitand loss results for each incremental set of parameters in that range.The computer will assume its role as a device that uses software andgenerates an output for each given input. The computer screen will then,when applicable, display the results of the output.

More specifically, the software of the present invention preferably willinclude the following features and capabilities. An icon will be made togenerate the PAP/VC chart screen. This icon will generate a PAP and/or aVC on the screen with preset functions or customizable functions. ThePAP and/or VC will be generated from the bar chart (price chart) locatedin the primary charting position on the screen. Either the VC or PAP canbe generated individually without the traditional price chart, if sodesired.

1) Options for PAP/VC

-   -   a) We will assume that the user has generated a bar chart (price        chart) of a underlying market. The user can then generate a        Value Chart™ and/or a Price Action Profile™ on the same screen.        The VC will be preferably positioned below the traditional price        chart. The PAP will preferably be embodied in a resizable window        and placed anywhere on the screen. The VC can be displayed by        itself. The PAP can be displayed by itself. Several VCs        representing different timeframes of one market or different        markets can be displayed on the same display. Several PAPs        representing different timeframes of one market or different        markets can be displayed on the same display.    -   b) The Floating Axis can be either preset or user defined. The        Floating Axis can be any function of price.    -   c) The Dynamic Volatility Units can be either preset or user        defined. The DVU can be any function of price but preferably        should be a function of market volatility.    -   d) User either has the option to define relative overbought and        oversold conventions or they will be preset (see FIG. 22 and        FIG. 23 b).    -   e) User can change the time period of the Value Chart™ to a        shorter period and leave the PAP/VC on the designated longer        period. For example, user can use a Daily PAP/VC and elect to        have 60-minute bars displayed on the daily VC axis.    -   f) User can select different colors for each DVU sector interval        of PAP and VC.    -   g) User has option to specify data file for PAP to access in        order to get more data samples for bell curve.        (C:\Genesis\Commodity\Tbonds.dat)    -   h) User can set an alarm (audio or graphical) that notifies user        if market has penetrated designated VC price level or deviation        from floating axis.    -   i) User can use other indicators and market analysis tools in        conjunction with VC and PAP concepts.    -   j) User has option to resize the PAP/VC portion of the screen.    -   k) If user selects to display multiple VCs on the same screen,        the user can elect to have the charts share the same time axis        or have independent time axes (so that the short-term charts are        not so spread out.)    -   l) User can elect to have elect to hide/display PAP for each VC.        User can display PAP individually.    -   m) Have a pointer that will show the corresponding Traditional        bar corresponding to the Value Chart™ that the user has        highlighted (Vertical line through the bar, arrows over/under        bar, bar changes color.)    -   n) Have Overbought/Fair Value/Oversold test box(es) to the right        of the VC. The user can engage/disable these markers. User can        make these markers turn a certain color of flash when the price        is trading in their range.    -   o) When user expands/compress space between bars the same        spacing will be applied to the VC with the same time period        bars.    -   p) Use can color traditional price chart different colors or tag        price bars when they trade in designated VC price levels or        ranges or meet certain criterion involving VC and or PAP        concept. For example, if momentum is decreasing and VC is        trading under the −8 VC level, then color VC and traditional and        VC price bars red (or any color) and/or tag bars with a dot        under them.    -   q) VC and PAP scales for the DVUs can be preset or user defined.    -   r) Have option where traditional primary bar chart bars can        change to the color of the overbought/fair value/oversold range        that the VC is trading in, assuming that the VC valuation        sectors have been designated with different colors.    -   s) Give the users the ability to create conditional profiles.        For example, if condition A is met then collect bars for the PAP        else do not collect bars for the profile. If the condition A        identified a rising market then the PAP could identify a typical        bell curve in rising markets and give the user a better idea of        realistic entry and exit points.    -   t) Give user ability to color bars in both VC charts and        traditional bar charts and plot them on the same screen or split        screens. The user can adjust their scales to view them apart if        he wants. The user can elect to format or remove the gridlines        on either chart.    -   u) Give user option to color sectors and axis lines        (specifically the zero axis) on the Value Charts™.    -   v) Give user the option to color the portions of the traditional        bar chart the same as the sector colors that each section of        each bars lies in on the Value Chart™. This coloring tool would        color each single bar different colors as deemed by the        corresponding sectors in Value Charts™ that each bar was located        in.    -   w) Users can set alarms that would sound if short-term momentum        slowed and the market was overbought/oversold in Value Chart™ by        N points. Other custom conditions could be programmed by the        user.    -   x) Users can draw trend lines on the Value Chart™ and real        placement of the trend line would be also placed on the regular        bar chart. It would be likely that the tend line would appear        non-linear on the regular bar chart.    -   y) Have a small data window option that shows the absolute        values for the Floating Axis and the DMU Interval and any VC        prices for any date or time period.    -   aa) Have an icon that allows the user to toggle between his        normal profile and a “quick check market strength” profile for N        number of bars. If the user wants to know what the market        strength for the last 10 days is, he hits the icon and this        profile will be plotted along with its characteristics (which        will compare the buying and/or selling strength has been for the        most recent 10 days compared to that same period historically.        The profile could show how much activity is above/below the zero        line and what the spread of the profile is compared to        historical spreads, etc. The following characteristics could be        disclosed:        -   A) Profile Activity above/below zero line.        -   B) Standard Deviation.    -   bb) Create an indicator that tells how much exposure        (most/least/average can be displayed in $ or DVU points), in the        days following a market entry, an investor would have had in his        historical testing if he would have bought the market at the        value of −4 on the Value Chart™. This study could create a graph        that shows the most, least, and average exposure for each N day        period after a defined market entry. Another chart that could be        generated could include an average low exposure for long        positions and an average high line for short positions (they        could be plotted on a traditional bar chart or a VC.) This study        can also be done when user defined conditions are met (i.e.        price >average(Close,10) and buy at −4 value on VC.).

The above features represent some of the features that the Value Chartsand Price Action Profiles concepts may include. The attached figuresshow potential layouts for the visual displays, but are not inclusive ofall of the possible combinations.

Alternatively, the concepts may be implemented in an on-line system orglobal computer network, or even providing hard copies of the charts andinformation generated. These are also just examples of how the conceptscan be implemented, and any other approach is within the scope of theinvention.

1. A method for facilitating the making of a trading decision by aninvestor, said method comprising the steps of: receiving a collection ofprice data relating to an investment from a data source in a processor;processing said collection of price data related to an investment togenerate volatility-adjusted relative price data related to theinvestment; and generating at least one price chart derived from saidprocessing step, wherein the at least one price chart includes avolatility-adjusted relative price chart representing a plurality ofvolatility-adjusted relative price data related to dynamic volatilityintervals for the investment, wherein the at least one price chartprovides an indication of whether the investment is trading at a fairvalue, undervalued or overvalued conditions.
 2. The method of claim 1,further comprising the step of displaying a volatility-adjusted relativeprice chart on a display.
 3. The method of claim 2, further comprisingthe display of said plurality of volatility-adjusted relative price datawith a market status indicator identifying market valuation.
 4. Themethod of claim 3, wherein the market valuation is set forth in termsselected from the group consisting of fair value, overbought andoversold market conditions.
 5. The method of claim 1, wherein saidvolatility-adjusted relative price chart is produced, at least in part,during said processing step by plotting y-axis data points as deviationsof price above or below a floating axis for each unit of time on thex-axis, the floating axis representing price function (F), and they-axis price units being defined in terms of a dynamic volatilityinterval function (I) resulting in a plurality of saidvolatility-adjusted relative price data plotted with respect to saidfloating axis for each x-axis time unit.
 6. The method of claim 5,wherein at least one of said plurality of price charts includes a priceaction profile which is derived from said volatility-adjusted relativeprice chart and wherein said price action profile is produced, at leastin part, during said processing step by determining a trading frequencyfor said volatility-adjusted relative price data within each saiddynamic volatility interval on said volatility-adjusted relative pricechart, by calculating a percentage for said trading frequency withineach dynamic volatility interval with respect to a total sum of all saidvolatility-adjusted relative price chart trading activity and byplotting said percentage associated with said trading frequency for eachdynamic volatility interval to yield a distribution reflecting thevolatility-adjusted relative price representing trading activity withdynamic volatility interval.
 7. The method of claim 6, furthercomprising the display of said volatility-adjusted relative price chartand said price action profile proximately to one another on a display.8. The method of claim 6, wherein the data derived from said priceaction profile is in a tabular format, text format, or a graphicalformat.
 9. The method of claim 6, further comprising the display of saidprice action profile with a market status indicator identifying marketvaluation in terms selected from or related to fair value, overbought,overvalued, oversold or undervalued market conditions.
 10. The methodclaim 6, further comprising combining the information derived from theprice action profile with other market indicator information.
 11. Themethod of claim 6, further comprising the step of showing said priceaction profile on a display.
 12. The method of claim 6, furthercomprising developing a plurality of price action profiles from aplurality of different time frames.
 13. The method of claim 12, whereinthe plurality of different price action profiles are selected from thegroup consisting of a single market and different markets.
 14. Themethod of claim 1, further comprising the display of a plurality ofcharts with one or more comprising said volatility-adjusted relativeprice chart in a plurality of time frames on a display.
 15. The methodof claim 1, wherein said volatility-adjusted relative price data withineach said dynamic volatility interval on said volatility-adjustedrelative price chart is recorded only when a predetermined condition ismet.
 16. The method of claim 15, wherein the at least one price chartincludes a conditional price action profile which is derived from saidvolatility-adjusted relative price chart and wherein said conditionalprice action profile is produced, at least in part, during saidprocessing step by determining a trading frequency for saidvolatility-adjusted relative price data recorded within each saiddynamic volatility interval, by determining a percentage for saidtrading frequency with respect to a total trading activity and byplotting said percentage associated with said trading frequency to yielda conditional relative frequency distribution.
 17. The method of claim1, wherein the price chart is in a form selected from the groupconsisting of tabular format, text format, or a graphical format. 18.The method of claim 1, further comprising information derived fromprocessing said collection of price data for output is used for furtheranalysis within other market analysis algorithms.
 19. The method ofclaim 1, further comprising using information derived from avolatility-adjusted relative price chart to apply to absolute pricecharts.
 20. The method of claim 1, further comprising combining theinformation derived from the volatility-adjusted relative price chartwith other market indicator information.
 21. The method according toclaim 1, wherein the information is used by mathematical trading systemto enter or exit an investment on behalf of an investor according tocriteria input into the mathematical trading system.
 22. A method forfacilitating the making of a trading decision by an investor, saidmethod comprising the steps of: receiving a collection of price datarelating to an investment from a data source in a processor; processingsaid collection of price data related to an investment to generatevolatility-adjusted relative price data related to the investment; andgenerating an indication of a state of a market for the investment asbeing currently traded at a fair value, as overvalued or as undervaluedby assessment of the volatility-adjusted relative price data in relationto dynamic volatility intervals determined for the investment, whereinthe indication is used by an investor to make a trading decision as toentering or exiting an investment.
 23. The method of claim 22, whereinthe indication is used by mathematical trading system to enter or exitan investment on behalf of an investor according to criteria input intothe mathematical trading system.